Scenefiltering can be hazardous to both your mind and body if used extensively. Avoid scenefiltering if possible.
If you're an aspiring young encoder or someone who has been around fansubbing for a while, you've probably heard the term "scenefiltering". But what is scenefiltering? As the name suggests, it is simply filtering different scenes or frames of a video clip distinctly.
Normally, if you have a source that has great video quality with minimal video artifacts, you can use a simple chain of filters on the entire video without any concern. However, if you have a more complex source with a myriad of video artefacts, you probably don't want to use the same filters everywhere. For instance, one scene could have heavy banding while another scene might have strong aliasing. If you were to fix both of these issues by using strong filtering over the entire video, it would likely result in detail loss in other scenes, which you do not want. This is where scenefiltering comes in.
As always, you start by importing the VapourSynth module and loading your video source:
import vapoursynth as vs # this can look different based on your editor core = vs.core src = core.lsmas.LWLibavSource("source.m2ts")
Next, you need to choose what filtering will be done to the entire clip. Some filtering—like resizing in this example—may need to be put before any other filtering. At this stage, you can also come up with the default filters that need to be in a certain order, but will still be applied to the entire clip. If you can't come up with anything suitable, don't fret; you'll have plenty more chances to filter later.
filtered = core.resize.Bilinear(src, width=1280, height=720) # will occur at the deband stage, but for entire clip default_deband = deband(filtered)
Now that you have your common filtering down, you need to create some base filter chains. Go through some random scenes in your source and write down parts of the filtering that best suits those scenes. You should separate these as variables with proper names and sorting (group filters by their type) to keep everything neat and clean. If you do this part well, you will save yourself a lot of time later on, so take your time. At this point, your script should look something like this:
import vapoursynth as vs core = vs.core src = core.lsmas.LWLibavSource("source.m2ts") resized = core.resize.Bilinear(src, width=1280, height=720) light_denoise = some_denoise_filter(resized) heavy_denoise = some_other_denoise_filter(resized) denoised = ... aa = antialiasing(denoised) aa = ... default_deband = deband(aa) light_deband = deband1(aa) medium_deband = deband2(aa) debanded = ...
Once you've done all of that, you're done with filtering your source—at
least for the most part. Now all you need to do is add
ReplaceFramesSimple calls. For this, you need either the
plugin RemapFrames or
the native Python version in
Rfs is a shorthand for
and fvsfunc has the alias
import vapoursynth as vs core = vs.core src = core.lsmas.LWLibavSource("source.m2ts") resized = core.resize.Bilinear(src, width=1280, height=720) ### Denoising light_denoise = some_denoise_filter(resized) heavy_denoise = some_other_denoise_filter(resized) heavier_denoise = some_stronger_denoise_filter(resized) denoised = core.remap.Rfs(resized, light_denoise, mappings="") denoised = core.remap.Rfs(denoised, heavy_denoise, mappings="") denoised = core.remap.Rfs(denoised, heavier_denoise, mappings="") ### Anti-aliasing eedi2_aa = eedi2_aa_filter(denoised) nnedi3_aa = nnedi3_aa_filter(denoised) aa = core.remap.Rfs(denoised, eedi2_aa, mappings="") aa = core.remap.Rfs(aa, nnedi3_aa, mappings="") ### Debanding default_deband = default_deband(aa) light_deband = deband1(aa) medium_deband = deband2(aa) debanded = default_deband # will apply filter to the entire clip debanded = core.remap.Rfs(debanded, light_deband, mappings="") debanded = core.remap.Rfs(debanded, med_deband, mappings="")
So you created all your base filters and added Rfs calls. Now what? You still have to perform the most tedious part of this entire process—adding frame ranges to those calls. The basic workflow is quite simple:
- Go to the start of the scene. View the next 2-3 frames. Go to the end of the scene. View the previous 2-3 frames. Based on this, decide on your filtering for the particular scene. If still in doubt, look at other frames in the scene. Sometimes, you will find that different frames in the same scene require different filtering, but this is quite uncommon.
Now that you know what filter to use, simply add the frame range to the respective Rfs call. To add a frame range to Rfs, you need to enter it as a string in the
mappingsparameter. The format for the string is
[start_frame end_frame]. If you only want to add a single frame, the format is
frame_number. An example should help you understand better:
# The following replaces frames 30 to 40 (inclusive) and frame 50 # of the base clip with the filtered clip. filtered = core.remap.Rfs(base, filtered, mappings="[30 40] 50")
Repeat with the next scene.
When scenefiltering, it is good practice to comment out Rfs calls you're currently not using because they just make your script slower and eat up memory.
This step can take anywhere from a few minutes to hours, depending on the encoder and the source. Most of the time, the same filters can be reused every episode with some minor changes here and there.
Now you might ask, "Why did I have to create base filters for
everything?" The answer is that these base filters allow other filters
to be added on top of them. Let's say a scene requires
but also needs
medium_deband on top of that. Just put the same frame
ranges in their Rfs calls and watch it happen. What if a scene requires
denoising stronger than
heavier_denoise ? Simple. Add another denoising
filter instead of
heavier_denoise like so:
super_heavy_denoise = ultra_mega_super_heavy_denoise(filtered) filtered = core.remap.Rfs(filtered, super_heavy_denoise, mappings="[x y]")
Using different denoisers on that same frame range is also possible, but always consider the impacts on performance. Calling a strong, slow denoise filter might still be faster (and better-looking) than calling a weak, faster filter multiple times.
If using VSEdit as your editor,
it can be helpful to use the
built-in bookmark functionality
to find the frame ranges of each scene.
There is a small script that can generate
these bookmarks from your clip inside of VSEdit.
If you already have a keyframe file
(WWXD qp-file or Xvid keyframes)
you can instead use the
# Editing a script called 'example01.vpy' import ... from vsbookmark import generate generate(clip, 'example01') #convert('keyframes.txt', 'example01') clip.set_output()
When previewing your clip, there will now be bookmarks generated on the timeline allowing you to skip to the next scene using the GUI buttons.
1. The python script may be slower than the plug-in due to the way it calls std.Splice to combine multiple re-mappings. The plug-in on the other hand, directly serves the frames of the second clip, with no calls to Splice. The speed difference will likely only be noticeable with a large amount of re-mappings. So, for the average script, it should be unnoticeable. ↩